Overview

Dataset statistics

Number of variables24
Number of observations71
Missing cells156
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory193.8 B

Variable types

Numeric20
Categorical2
Unsupported2

Warnings

df_aicr_previous_week has 11 (15.5%) missing values Missing
df_itg20_previous_week has 3 (4.2%) missing values Missing
df_ntt_previous_week has 71 (100.0%) missing values Missing
df_sentiment_previous_week has 71 (100.0%) missing values Missing
Unnamed: 0 has unique values Unique
employeeid has unique values Unique
df_itg_previous_week has unique values Unique
df_aht_previous_week has unique values Unique
df_ntt_previous_week is an unsupported type, check if it needs cleaning or further analysis Unsupported
df_sentiment_previous_week is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 0 has 1 (1.4%) zeros Zeros
Friday has 10 (14.1%) zeros Zeros
Monday has 4 (5.6%) zeros Zeros
Saturday has 1 (1.4%) zeros Zeros
Sunday has 3 (4.2%) zeros Zeros
Thursday has 5 (7.0%) zeros Zeros
Tuesday has 4 (5.6%) zeros Zeros
Wednesday has 6 (8.5%) zeros Zeros
previous week_x has 11 (15.5%) zeros Zeros
actual week has 16 (22.5%) zeros Zeros
df_aicr_previous_week has 1 (1.4%) zeros Zeros
df_transfer_previous_week has 3 (4.2%) zeros Zeros
df_tsr_previous_week has 54 (76.1%) zeros Zeros

Reproduction

Analysis started2021-05-19 16:54:57.479150
Analysis finished2021-05-19 16:55:47.122736
Duration49.64 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIQUE
ZEROS

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.5633803
Minimum0
Maximum262
Zeros1
Zeros (%)1.4%
Memory size696.0 B
2021-05-19T11:55:47.196162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.5
Q161.5
median100
Q3167
95-th percentile246
Maximum262
Range262
Interquartile range (IQR)105.5

Descriptive statistics

Standard deviation73.01990578
Coefficient of variation (CV)0.6429881322
Kurtosis-0.7891789255
Mean113.5633803
Median Absolute Deviation (MAD)58
Skewness0.3248802852
Sum8063
Variance5331.90664
MonotocityStrictly increasing
2021-05-19T11:55:47.343178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
1.4%
801
 
1.4%
1971
 
1.4%
891
 
1.4%
871
 
1.4%
861
 
1.4%
2101
 
1.4%
811
 
1.4%
791
 
1.4%
951
 
1.4%
Other values (61)61
85.9%
ValueCountFrequency (%)
01
1.4%
11
1.4%
21
1.4%
71
1.4%
101
1.4%
131
1.4%
151
1.4%
161
1.4%
181
1.4%
201
1.4%
ValueCountFrequency (%)
2621
1.4%
2591
1.4%
2511
1.4%
2481
1.4%
2441
1.4%
2351
1.4%
2321
1.4%
2281
1.4%
2151
1.4%
2101
1.4%

employeeid
Real number (ℝ≥0)

UNIQUE

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2257558.239
Minimum518803
Maximum3799196
Zeros0
Zeros (%)0.0%
Memory size696.0 B
2021-05-19T11:55:47.477050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum518803
5-th percentile806967
Q11442818.5
median2310754
Q33119998
95-th percentile3504312
Maximum3799196
Range3280393
Interquartile range (IQR)1677179.5

Descriptive statistics

Standard deviation935007.372
Coefficient of variation (CV)0.4141675531
Kurtosis-1.222001082
Mean2257558.239
Median Absolute Deviation (MAD)816947
Skewness-0.07949731667
Sum160286635
Variance8.742387858 × 1011
MonotocityNot monotonic
2021-05-19T11:55:47.609254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34242561
 
1.4%
23683541
 
1.4%
12803521
 
1.4%
32050851
 
1.4%
34252571
 
1.4%
12702311
 
1.4%
34269021
 
1.4%
17687881
 
1.4%
19716621
 
1.4%
23107541
 
1.4%
Other values (61)61
85.9%
ValueCountFrequency (%)
5188031
1.4%
5969271
1.4%
7073501
1.4%
7873121
1.4%
8266221
1.4%
9061081
1.4%
9532171
1.4%
10017541
1.4%
11367491
1.4%
11409291
1.4%
ValueCountFrequency (%)
37991961
1.4%
37357991
1.4%
36831041
1.4%
35670851
1.4%
34415391
1.4%
34348861
1.4%
34269021
1.4%
34252571
1.4%
34242561
1.4%
34231021
1.4%

Friday
Real number (ℝ)

ZEROS

Distinct40
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.639925846
Minimum-100
Maximum100
Zeros10
Zeros (%)14.1%
Memory size696.0 B
2021-05-19T11:55:47.740872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-41.79912698
median-2.673846154
Q326.11111111
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)67.9102381

Descriptive statistics

Standard deviation52.02315726
Coefficient of variation (CV)-6.80937987
Kurtosis-0.2040604544
Mean-7.639925846
Median Absolute Deviation (MAD)36.00717949
Skewness0.1325723123
Sum-542.434735
Variance2706.408891
MonotocityNot monotonic
2021-05-19T11:55:47.865308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
010
 
14.1%
-1007
 
9.9%
1005
 
7.0%
-504
 
5.6%
-66.666666674
 
5.6%
33.333333334
 
5.6%
-253
 
4.2%
-33.333333332
 
2.8%
-32.3551
 
1.4%
-16.666666671
 
1.4%
Other values (30)30
42.3%
ValueCountFrequency (%)
-1007
9.9%
-66.666666674
5.6%
-601
 
1.4%
-52.838888891
 
1.4%
-504
5.6%
-42.857142861
 
1.4%
-40.741111111
 
1.4%
-33.333333332
 
2.8%
-32.3551
 
1.4%
-30.83751
 
1.4%
ValueCountFrequency (%)
1005
7.0%
66.666666671
 
1.4%
64.285714291
 
1.4%
50.666666671
 
1.4%
501
 
1.4%
46.6681
 
1.4%
42.857142861
 
1.4%
40.833333331
 
1.4%
37.51
 
1.4%
33.333333334
5.6%

Monday
Real number (ℝ)

ZEROS

Distinct46
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.989919375
Minimum-100
Maximum100
Zeros4
Zeros (%)5.6%
Memory size696.0 B
2021-05-19T11:55:47.996368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-28.57142857
median0
Q340.83333333
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)69.4047619

Descriptive statistics

Standard deviation57.61866003
Coefficient of variation (CV)-28.95527365
Kurtosis-0.5579188587
Mean-1.989919375
Median Absolute Deviation (MAD)35.71428571
Skewness-0.1379158312
Sum-141.2842756
Variance3319.909984
MonotocityNot monotonic
2021-05-19T11:55:48.121810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
-10010
 
14.1%
1006
 
8.5%
506
 
8.5%
04
 
5.6%
-22.222222222
 
2.8%
-28.571428572
 
2.8%
-252
 
2.8%
-19.046428571
 
1.4%
-13.333333331
 
1.4%
-14.285714291
 
1.4%
Other values (36)36
50.7%
ValueCountFrequency (%)
-10010
14.1%
-83.3351
 
1.4%
-71.428571431
 
1.4%
-601
 
1.4%
-501
 
1.4%
-44.444444441
 
1.4%
-35.714285711
 
1.4%
-33.333333331
 
1.4%
-28.571428572
 
2.8%
-252
 
2.8%
ValueCountFrequency (%)
1006
8.5%
751
 
1.4%
71.428571431
 
1.4%
601
 
1.4%
58.333333331
 
1.4%
57.142857141
 
1.4%
506
8.5%
41.666666671
 
1.4%
401
 
1.4%
36.363636361
 
1.4%

Saturday
Real number (ℝ)

ZEROS

Distinct44
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.243727705
Minimum-100
Maximum100
Zeros1
Zeros (%)1.4%
Memory size696.0 B
2021-05-19T11:55:48.258279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-34.52380952
median7.14
Q336.04166667
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)70.56547619

Descriptive statistics

Standard deviation60.49237511
Coefficient of variation (CV)48.6379574
Kurtosis-0.7080026358
Mean1.243727705
Median Absolute Deviation (MAD)40.47333333
Skewness-0.0007654316641
Sum88.30466703
Variance3659.327446
MonotocityNot monotonic
2021-05-19T11:55:48.396410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
10010
 
14.1%
-1009
 
12.7%
-33.333333333
 
4.2%
-203
 
4.2%
-503
 
4.2%
33.333333333
 
4.2%
502
 
2.8%
-37.52
 
2.8%
-31.251251
 
1.4%
21.212727271
 
1.4%
Other values (34)34
47.9%
ValueCountFrequency (%)
-1009
12.7%
-73.333333331
 
1.4%
-66.666666671
 
1.4%
-601
 
1.4%
-503
 
4.2%
-37.52
 
2.8%
-35.714285711
 
1.4%
-33.333333333
 
4.2%
-31.251251
 
1.4%
-29.166666671
 
1.4%
ValueCountFrequency (%)
10010
14.1%
801
 
1.4%
62.51
 
1.4%
601
 
1.4%
502
 
2.8%
40.833333331
 
1.4%
401
 
1.4%
38.751
 
1.4%
33.333333333
 
4.2%
27.500833331
 
1.4%

Sunday
Real number (ℝ)

ZEROS

Distinct50
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.8592477
Minimum-100
Maximum100
Zeros3
Zeros (%)4.2%
Memory size696.0 B
2021-05-19T11:55:48.595813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-23.80952381
median11.28968254
Q341.46825397
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)65.27777778

Descriptive statistics

Standard deviation52.97902826
Coefficient of variation (CV)4.878701521
Kurtosis-0.1320840503
Mean10.8592477
Median Absolute Deviation (MAD)31.13431746
Skewness-0.1801109569
Sum771.0065865
Variance2806.777435
MonotocityNot monotonic
2021-05-19T11:55:48.804552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009
 
12.7%
-1005
 
7.0%
504
 
5.6%
03
 
4.2%
253
 
4.2%
-33.333333332
 
2.8%
16.666666672
 
2.8%
7.4074074071
 
1.4%
10.5171
 
1.4%
31.248751
 
1.4%
Other values (40)40
56.3%
ValueCountFrequency (%)
-1005
7.0%
-73.333333331
 
1.4%
-66.666666671
 
1.4%
-501
 
1.4%
-44.444444441
 
1.4%
-40.555444441
 
1.4%
-37.51
 
1.4%
-37.222222221
 
1.4%
-33.333333332
 
2.8%
-33.331
 
1.4%
ValueCountFrequency (%)
1009
12.7%
801
 
1.4%
66.666666671
 
1.4%
504
5.6%
47.223333331
 
1.4%
42.4241
 
1.4%
42.10317461
 
1.4%
40.833333331
 
1.4%
37.51
 
1.4%
35.690235691
 
1.4%

Thursday
Real number (ℝ)

ZEROS

Distinct44
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.278733149
Minimum-100
Maximum100
Zeros5
Zeros (%)7.0%
Memory size696.0 B
2021-05-19T11:55:48.973197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-30.3119697
median5.604615385
Q340.1825
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)70.4944697

Descriptive statistics

Standard deviation57.8796251
Coefficient of variation (CV)10.96468101
Kurtosis-0.5218600959
Mean5.278733149
Median Absolute Deviation (MAD)34.77128205
Skewness-0.2469404444
Sum374.7900536
Variance3350.051001
MonotocityNot monotonic
2021-05-19T11:55:49.122447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1008
 
11.3%
-1008
 
11.3%
05
 
7.0%
33.333333335
 
7.0%
-33.333333333
 
4.2%
503
 
4.2%
-502
 
2.8%
-451
 
1.4%
751
 
1.4%
2.2216666671
 
1.4%
Other values (34)34
47.9%
ValueCountFrequency (%)
-1008
11.3%
-83.333333331
 
1.4%
-751
 
1.4%
-66.666666671
 
1.4%
-502
 
2.8%
-451
 
1.4%
-33.333333333
 
4.2%
-31.457272731
 
1.4%
-29.166666671
 
1.4%
-25.39682541
 
1.4%
ValueCountFrequency (%)
1008
11.3%
751
 
1.4%
66.666251
 
1.4%
62.51
 
1.4%
601
 
1.4%
59.166666671
 
1.4%
503
 
4.2%
44.444444441
 
1.4%
40.3651
 
1.4%
401
 
1.4%

Tuesday
Real number (ℝ)

ZEROS

Distinct45
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2231065721
Minimum-100
Maximum100
Zeros4
Zeros (%)5.6%
Memory size696.0 B
2021-05-19T11:55:49.284757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-50
median1.763571429
Q335.68181818
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)85.68181818

Descriptive statistics

Standard deviation59.01610094
Coefficient of variation (CV)-264.5197781
Kurtosis-0.8460382192
Mean-0.2231065721
Median Absolute Deviation (MAD)48.23642857
Skewness-0.05532871238
Sum-15.84056662
Variance3482.90017
MonotocityNot monotonic
2021-05-19T11:55:49.438719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
-1007
 
9.9%
1006
 
8.5%
04
 
5.6%
-504
 
5.6%
503
 
4.2%
33.333333333
 
4.2%
602
 
2.8%
752
 
2.8%
-66.666666672
 
2.8%
-33.333333332
 
2.8%
Other values (35)36
50.7%
ValueCountFrequency (%)
-1007
9.9%
-87.51
 
1.4%
-83.333333331
 
1.4%
-71.428571431
 
1.4%
-66.666666672
 
2.8%
-601
 
1.4%
-58.333333331
 
1.4%
-52.338181821
 
1.4%
-504
5.6%
-44.444444441
 
1.4%
ValueCountFrequency (%)
1006
8.5%
85.714285711
 
1.4%
801
 
1.4%
752
 
2.8%
66.666666671
 
1.4%
602
 
2.8%
59.166666671
 
1.4%
503
4.2%
36.363636361
 
1.4%
351
 
1.4%

Wednesday
Real number (ℝ)

ZEROS

Distinct40
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.501150488
Minimum-100
Maximum100
Zeros6
Zeros (%)8.5%
Memory size696.0 B
2021-05-19T11:55:49.609553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-47.22222222
median-11.11111111
Q322.79153333
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)70.01375556

Descriptive statistics

Standard deviation56.33680204
Coefficient of variation (CV)-7.510421519
Kurtosis-0.3200327011
Mean-7.501150488
Median Absolute Deviation (MAD)34.02777778
Skewness0.3125455379
Sum-532.5816847
Variance3173.835264
MonotocityNot monotonic
2021-05-19T11:55:49.735153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1008
 
11.3%
-1008
 
11.3%
06
 
8.5%
-506
 
8.5%
504
 
5.6%
-33.333333333
 
4.2%
-602
 
2.8%
-202
 
2.8%
-27.6681
 
1.4%
-401
 
1.4%
Other values (30)30
42.3%
ValueCountFrequency (%)
-1008
11.3%
-801
 
1.4%
-602
 
2.8%
-55.553333331
 
1.4%
-506
8.5%
-44.444444441
 
1.4%
-401
 
1.4%
-33.333333333
 
4.2%
-27.6681
 
1.4%
-27.2721
 
1.4%
ValueCountFrequency (%)
1008
11.3%
66.666666671
 
1.4%
50.666666671
 
1.4%
504
5.6%
33.333333331
 
1.4%
301
 
1.4%
251
 
1.4%
22.916666671
 
1.4%
22.66641
 
1.4%
16.666666671
 
1.4%

previous week_x
Real number (ℝ)

ZEROS

Distinct28
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.50343172
Minimum-100
Maximum100
Zeros11
Zeros (%)15.5%
Memory size696.0 B
2021-05-19T11:55:49.861663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-33.33333333
median3.571428571
Q357.83923077
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)91.1725641

Descriptive statistics

Standard deviation64.53264029
Coefficient of variation (CV)6.14395771
Kurtosis-0.8400630443
Mean10.50343172
Median Absolute Deviation (MAD)46.42857143
Skewness-0.203456231
Sum745.743652
Variance4164.461663
MonotocityNot monotonic
2021-05-19T11:55:49.971267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
10014
19.7%
011
15.5%
-10010
14.1%
33.333333335
 
7.0%
-504
 
5.6%
-33.333333333
 
4.2%
602
 
2.8%
502
 
2.8%
-251
 
1.4%
-201
 
1.4%
Other values (18)18
25.4%
ValueCountFrequency (%)
-10010
14.1%
-504
 
5.6%
-46.6661
 
1.4%
-401
 
1.4%
-33.333333333
 
4.2%
-251
 
1.4%
-24.073333331
 
1.4%
-201
 
1.4%
-14.524285711
 
1.4%
011
15.5%
ValueCountFrequency (%)
10014
19.7%
91.66751
 
1.4%
66.666666671
 
1.4%
602
 
2.8%
55.678461541
 
1.4%
502
 
2.8%
48.610833331
 
1.4%
42.857142861
 
1.4%
35.714285711
 
1.4%
33.333333335
 
7.0%

actual week
Real number (ℝ)

ZEROS

Distinct24
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.8785409121
Minimum-100
Maximum100
Zeros16
Zeros (%)22.5%
Memory size696.0 B
2021-05-19T11:55:50.087620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-38.88833333
median0
Q350
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)88.88833333

Descriptive statistics

Standard deviation63.6235119
Coefficient of variation (CV)-72.41952084
Kurtosis-0.8008318032
Mean-0.8785409121
Median Absolute Deviation (MAD)50
Skewness-0.0711336444
Sum-62.37640476
Variance4047.951266
MonotocityNot monotonic
2021-05-19T11:55:50.195241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
016
22.5%
-10013
18.3%
10010
14.1%
506
 
8.5%
-503
 
4.2%
-33.333333332
 
2.8%
-252
 
2.8%
2.1157142862
 
2.8%
66.666666672
 
2.8%
311
 
1.4%
Other values (14)14
19.7%
ValueCountFrequency (%)
-10013
18.3%
-66.666666671
 
1.4%
-503
 
4.2%
-44.443333331
 
1.4%
-33.333333332
 
2.8%
-33.3321
 
1.4%
-252
 
2.8%
-19.047142861
 
1.4%
-14.58251
 
1.4%
016
22.5%
ValueCountFrequency (%)
10010
14.1%
751
 
1.4%
66.666666672
 
2.8%
506
8.5%
401
 
1.4%
33.333333331
 
1.4%
311
 
1.4%
251
 
1.4%
16.6651
 
1.4%
13.471
 
1.4%

label
Categorical

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
1
38 
0
33 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
138
53.5%
033
46.5%
2021-05-19T11:55:50.417209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-19T11:55:50.479726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
138
53.5%
033
46.5%

Most occurring characters

ValueCountFrequency (%)
138
53.5%
033
46.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71
100.0%

Most frequent character per category

ValueCountFrequency (%)
138
53.5%
033
46.5%

Most occurring scripts

ValueCountFrequency (%)
Common71
100.0%

Most frequent character per script

ValueCountFrequency (%)
138
53.5%
033
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII71
100.0%

Most frequent character per block

ValueCountFrequency (%)
138
53.5%
033
46.5%

df_aicr_previous_week
Real number (ℝ≥0)

MISSING
ZEROS

Distinct53
Distinct (%)88.3%
Missing11
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean60.99733333
Minimum0
Maximum100
Zeros1
Zeros (%)1.4%
Memory size696.0 B
2021-05-19T11:55:50.561416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.29075
Q155.54375
median61.1525
Q366.67125
95-th percentile75.5405
Maximum100
Range100
Interquartile range (IQR)11.1275

Descriptive statistics

Standard deviation13.36918603
Coefficient of variation (CV)0.2191765656
Kurtosis7.354365358
Mean60.99733333
Median Absolute Deviation (MAD)5.5575
Skewness-1.295025924
Sum3659.84
Variance178.7351351
MonotocityNot monotonic
2021-05-19T11:55:50.690928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
504
 
5.6%
753
 
4.2%
66.672
 
2.8%
60.712
 
2.8%
58.751
 
1.4%
48.5551
 
1.4%
59.631
 
1.4%
64.7751
 
1.4%
65.7151
 
1.4%
54.6151
 
1.4%
Other values (43)43
60.6%
(Missing)11
 
15.5%
ValueCountFrequency (%)
01
 
1.4%
30.951
 
1.4%
42.221
 
1.4%
46.5051
 
1.4%
48.551
 
1.4%
48.5551
 
1.4%
504
5.6%
54.041
 
1.4%
54.6151
 
1.4%
551
 
1.4%
ValueCountFrequency (%)
1001
 
1.4%
82.1451
 
1.4%
81.821
 
1.4%
75.211
 
1.4%
753
4.2%
72.9151
 
1.4%
72.811
 
1.4%
72.731
 
1.4%
71.571
 
1.4%
71.431
 
1.4%

df_cpc_previous_week
Real number (ℝ≥0)

Distinct69
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6721559153
Minimum0.23
Maximum1.11
Zeros0
Zeros (%)0.0%
Memory size696.0 B
2021-05-19T11:55:50.821530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.365625
Q10.5779166667
median0.6583333333
Q30.77125
95-th percentile0.9604166667
Maximum1.11
Range0.88
Interquartile range (IQR)0.1933333333

Descriptive statistics

Standard deviation0.1688883684
Coefficient of variation (CV)0.2512636794
Kurtosis0.3899603739
Mean0.6721559153
Median Absolute Deviation (MAD)0.09916666667
Skewness-0.02663013817
Sum47.72306999
Variance0.02852328099
MonotocityNot monotonic
2021-05-19T11:55:50.946339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.712
 
2.8%
12
 
2.8%
0.56251
 
1.4%
0.588751
 
1.4%
0.5251
 
1.4%
0.381251
 
1.4%
0.7231
 
1.4%
0.83251
 
1.4%
0.78083333331
 
1.4%
0.88142857141
 
1.4%
Other values (59)59
83.1%
ValueCountFrequency (%)
0.231
1.4%
0.331
1.4%
0.3311
1.4%
0.351
1.4%
0.381251
1.4%
0.42166666671
1.4%
0.42251
1.4%
0.47909090911
1.4%
0.51251
1.4%
0.5251
1.4%
ValueCountFrequency (%)
1.111
1.4%
12
2.8%
0.981
1.4%
0.94083333331
1.4%
0.92714285711
1.4%
0.88142857141
1.4%
0.8751
1.4%
0.87251
1.4%
0.86285714291
1.4%
0.8571
1.4%

df_itg_previous_week
Real number (ℝ≥0)

UNIQUE

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.21186687
Minimum12.5
Maximum94.12
Zeros0
Zeros (%)0.0%
Memory size696.0 B
2021-05-19T11:55:51.071741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.5
5-th percentile34.282
Q149.471875
median52.922
Q363.09791667
95-th percentile81.5825
Maximum94.12
Range81.62
Interquartile range (IQR)13.62604167

Descriptive statistics

Standard deviation13.79573826
Coefficient of variation (CV)0.2454239475
Kurtosis1.367666536
Mean56.21186687
Median Absolute Deviation (MAD)6.382
Skewness0.01890716108
Sum3991.042548
Variance190.3223942
MonotocityNot monotonic
2021-05-19T11:55:51.194848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.3241
 
1.4%
50.271
 
1.4%
82.826666671
 
1.4%
64.58251
 
1.4%
94.121
 
1.4%
50.313751
 
1.4%
52.381
 
1.4%
82.4651
 
1.4%
48.332857141
 
1.4%
12.51
 
1.4%
Other values (61)61
85.9%
ValueCountFrequency (%)
12.51
1.4%
23.8951
1.4%
31.951
1.4%
33.241
1.4%
35.3241
1.4%
39.6621
1.4%
44.703751
1.4%
45.50751
1.4%
45.831
1.4%
46.151
1.4%
ValueCountFrequency (%)
94.121
1.4%
82.826666671
1.4%
82.4651
1.4%
81.633333331
1.4%
81.531666671
1.4%
79.411
1.4%
74.6051
1.4%
74.481251
1.4%
73.8651
1.4%
73.4381
1.4%

df_itg20_previous_week
Real number (ℝ≥0)

MISSING

Distinct28
Distinct (%)41.2%
Missing3
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean0.9271867997
Minimum0.3333333333
Maximum1
Zeros0
Zeros (%)0.0%
Memory size696.0 B
2021-05-19T11:55:51.315392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.3333333333
5-th percentile0.6
Q10.9264285714
median1
Q31
95-th percentile1
Maximum1
Range0.6666666667
Interquartile range (IQR)0.07357142857

Descriptive statistics

Standard deviation0.1430386987
Coefficient of variation (CV)0.1542717161
Kurtosis5.479802152
Mean0.9271867997
Median Absolute Deviation (MAD)0
Skewness-2.421657368
Sum63.04870238
Variance0.02046006933
MonotocityNot monotonic
2021-05-19T11:55:51.418635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
140
56.3%
0.62
 
2.8%
0.94333333331
 
1.4%
0.751
 
1.4%
0.9451
 
1.4%
0.92857142861
 
1.4%
0.95751
 
1.4%
0.54251
 
1.4%
0.9851
 
1.4%
0.9951
 
1.4%
Other values (18)18
25.4%
(Missing)3
 
4.2%
ValueCountFrequency (%)
0.33333333331
1.4%
0.51
1.4%
0.54251
1.4%
0.62
2.8%
0.6251
1.4%
0.6321
1.4%
0.751
1.4%
0.8051
1.4%
0.80857142861
1.4%
0.85714285711
1.4%
ValueCountFrequency (%)
140
56.3%
0.9951
 
1.4%
0.9851
 
1.4%
0.9791
 
1.4%
0.9751
 
1.4%
0.95751
 
1.4%
0.9571
 
1.4%
0.9451
 
1.4%
0.94333333331
 
1.4%
0.9391
 
1.4%

df_aht_previous_week
Real number (ℝ≥0)

UNIQUE

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1197.18908
Minimum404.4
Maximum4714.75
Zeros0
Zeros (%)0.0%
Memory size696.0 B
2021-05-19T11:55:51.550525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum404.4
5-th percentile693.1111111
Q1923.6602564
median1042.714286
Q31290.184524
95-th percentile1861.785714
Maximum4714.75
Range4310.35
Interquartile range (IQR)366.5242674

Descriptive statistics

Standard deviation546.240597
Coefficient of variation (CV)0.4562692778
Kurtosis24.13984034
Mean1197.18908
Median Absolute Deviation (MAD)168.5714286
Skewness4.01688082
Sum85000.4247
Variance298378.7898
MonotocityNot monotonic
2021-05-19T11:55:51.670547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18771
 
1.4%
1007.71
 
1.4%
950.751
 
1.4%
1042.3333331
 
1.4%
932.251
 
1.4%
1133.11
 
1.4%
915.91
 
1.4%
1248.3333331
 
1.4%
16701
 
1.4%
1404.9166671
 
1.4%
Other values (61)61
85.9%
ValueCountFrequency (%)
404.41
1.4%
594.41
1.4%
680.31
1.4%
6931
1.4%
693.22222221
1.4%
7851
1.4%
817.51
1.4%
822.07142861
1.4%
856.51
1.4%
861.14285711
1.4%
ValueCountFrequency (%)
4714.751
1.4%
2118.751
1.4%
20251
1.4%
18771
1.4%
1846.5714291
1.4%
1709.51
1.4%
1687.51
1.4%
1683.3333331
1.4%
16731
1.4%
16701
1.4%

df_ntt_previous_week
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing71
Missing (%)100.0%
Memory size696.0 B

df_sentiment_previous_week
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing71
Missing (%)100.0%
Memory size696.0 B

df_surveycount_previous_week
Real number (ℝ≥0)

Distinct24
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.444152952
Minimum1
Maximum25.85714286
Zeros0
Zeros (%)0.0%
Memory size696.0 B
2021-05-19T11:55:51.779212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.666666667
95-th percentile4.785714286
Maximum25.85714286
Range24.85714286
Interquartile range (IQR)0.6666666667

Descriptive statistics

Standard deviation4.90665739
Coefficient of variation (CV)2.007508321
Kurtosis19.02664394
Mean2.444152952
Median Absolute Deviation (MAD)0
Skewness4.465750776
Sum173.5348596
Variance24.07528674
MonotocityNot monotonic
2021-05-19T11:55:51.885293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
140
56.3%
1.6666666674
 
5.6%
23
 
4.2%
1.53
 
4.2%
1.3333333332
 
2.8%
24.428571431
 
1.4%
1.41
 
1.4%
25.857142861
 
1.4%
5.7142857141
 
1.4%
2.1111111111
 
1.4%
Other values (14)14
 
19.7%
ValueCountFrequency (%)
140
56.3%
1.1428571431
 
1.4%
1.21
 
1.4%
1.2857142861
 
1.4%
1.3333333332
 
2.8%
1.41
 
1.4%
1.53
 
4.2%
1.61
 
1.4%
1.6666666674
 
5.6%
1.71
 
1.4%
ValueCountFrequency (%)
25.857142861
1.4%
25.666666671
1.4%
24.428571431
1.4%
5.7142857141
1.4%
3.8571428571
1.4%
3.7777777781
1.4%
3.0769230771
1.4%
2.7142857141
1.4%
2.3333333331
1.4%
2.2857142861
1.4%

df_transfer_previous_week
Real number (ℝ≥0)

ZEROS

Distinct69
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.851387141
Minimum0
Maximum44.16625
Zeros3
Zeros (%)4.2%
Memory size696.0 B
2021-05-19T11:55:52.003097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.23
Q15.23875
median8.526428571
Q311.18541667
95-th percentile25.78125
Maximum44.16625
Range44.16625
Interquartile range (IQR)5.946666667

Descriptive statistics

Standard deviation8.145344799
Coefficient of variation (CV)0.8268221198
Kurtosis6.508098565
Mean9.851387141
Median Absolute Deviation (MAD)3.138928571
Skewness2.271771226
Sum699.448487
Variance66.34664189
MonotocityNot monotonic
2021-05-19T11:55:52.123923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
4.2%
6.7471428571
 
1.4%
9.5051
 
1.4%
8.52251
 
1.4%
9.0528571431
 
1.4%
2.30751
 
1.4%
16.671
 
1.4%
9.6358333331
 
1.4%
4.0457142861
 
1.4%
12.412857141
 
1.4%
Other values (59)59
83.1%
ValueCountFrequency (%)
03
4.2%
0.79333333331
 
1.4%
1.6666666671
 
1.4%
2.30751
 
1.4%
2.3814285711
 
1.4%
2.5857142861
 
1.4%
3.031
 
1.4%
3.4516666671
 
1.4%
4.0457142861
 
1.4%
4.1666666671
 
1.4%
ValueCountFrequency (%)
44.166251
1.4%
40.9251
1.4%
30.088751
1.4%
26.56251
1.4%
251
1.4%
22.765714291
1.4%
20.746666671
1.4%
16.671
1.4%
16.5151
1.4%
15.131251
1.4%

df_tsr_previous_week
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6617634087
Minimum0
Maximum25
Zeros54
Zeros (%)76.1%
Memory size696.0 B
2021-05-19T11:55:52.223678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.395416667
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.231298842
Coefficient of variation (CV)4.882861154
Kurtosis48.66569351
Mean0.6617634087
Median Absolute Deviation (MAD)0
Skewness6.774111939
Sum46.98520202
Variance10.4412922
MonotocityNot monotonic
2021-05-19T11:55:52.325227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
054
76.1%
0.27666666671
 
1.4%
1.136251
 
1.4%
1.13751
 
1.4%
0.28916666671
 
1.4%
0.1351
 
1.4%
251
 
1.4%
1.6533333331
 
1.4%
0.66333333331
 
1.4%
0.961251
 
1.4%
Other values (8)8
 
11.3%
ValueCountFrequency (%)
054
76.1%
0.1351
 
1.4%
0.221
 
1.4%
0.2251
 
1.4%
0.27666666671
 
1.4%
0.28916666671
 
1.4%
0.30166666671
 
1.4%
0.35666666671
 
1.4%
0.55916666671
 
1.4%
0.56909090911
 
1.4%
ValueCountFrequency (%)
251
1.4%
11.111111111
1.4%
2.391
1.4%
1.6533333331
1.4%
1.13751
1.4%
1.136251
1.4%
0.961251
1.4%
0.66333333331
1.4%
0.56909090911
1.4%
0.55916666671
1.4%

Label
Categorical

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
1
42 
0
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
142
59.2%
029
40.8%
2021-05-19T11:55:52.529306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-19T11:55:52.592100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
142
59.2%
029
40.8%

Most occurring characters

ValueCountFrequency (%)
142
59.2%
029
40.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71
100.0%

Most frequent character per category

ValueCountFrequency (%)
142
59.2%
029
40.8%

Most occurring scripts

ValueCountFrequency (%)
Common71
100.0%

Most frequent character per script

ValueCountFrequency (%)
142
59.2%
029
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII71
100.0%

Most frequent character per block

ValueCountFrequency (%)
142
59.2%
029
40.8%

Score
Real number (ℝ≥0)

Distinct70
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7458619718
Minimum0.5003
Maximum0.9945
Zeros0
Zeros (%)0.0%
Memory size696.0 B
2021-05-19T11:55:52.671307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.5003
5-th percentile0.52115
Q10.6528
median0.7481
Q30.86765
95-th percentile0.9742
Maximum0.9945
Range0.4942
Interquartile range (IQR)0.21485

Descriptive statistics

Standard deviation0.1376262769
Coefficient of variation (CV)0.1845197666
Kurtosis-0.9140515097
Mean0.7458619718
Median Absolute Deviation (MAD)0.1035
Skewness-0.05522875697
Sum52.9562
Variance0.0189409921
MonotocityNot monotonic
2021-05-19T11:55:52.809039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78812
 
2.8%
0.64461
 
1.4%
0.53731
 
1.4%
0.98131
 
1.4%
0.76431
 
1.4%
0.6891
 
1.4%
0.65951
 
1.4%
0.90891
 
1.4%
0.89451
 
1.4%
0.97481
 
1.4%
Other values (60)60
84.5%
ValueCountFrequency (%)
0.50031
1.4%
0.50921
1.4%
0.5151
1.4%
0.51661
1.4%
0.52571
1.4%
0.52931
1.4%
0.53661
1.4%
0.53731
1.4%
0.551
1.4%
0.5571
1.4%
ValueCountFrequency (%)
0.99451
1.4%
0.9841
1.4%
0.98131
1.4%
0.97481
1.4%
0.97361
1.4%
0.92281
1.4%
0.92051
1.4%
0.91811
1.4%
0.91761
1.4%
0.91051
1.4%

Interactions

2021-05-19T11:55:00.945785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:01.071513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:01.367119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:01.484222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:01.602397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:01.723124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:01.829253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:01.932998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.036574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.139492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.240623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.334511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.428802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.531307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.632505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.719582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.815612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:02.906802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.000155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.092094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.193957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.298252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.404993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.561110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.667293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.772454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.878082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:03.995948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.127197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.238871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.337076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.432007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.546452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.657834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.758377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.862946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:04.963466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:05.064226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:05.164269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:05.425461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:05.545559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:05.653465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:05.764097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:05.885303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.002088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.117789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.234085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.350485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.461342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.564546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.699236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.820579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:06.926519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.020706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.128029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.231679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.335866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.432808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.537443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.647685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.759570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:07.869153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.006173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.117368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.224072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.327312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.431616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.536552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.637715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.744961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.862103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:08.969581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.066413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.179714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.289378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.391134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.492028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.598425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.708900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.819527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:09.931352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:10.046026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:10.349430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:10.464248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:10.576511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:10.691199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:10.797671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:10.901843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.010971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.130663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.241319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.344429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.457271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.573534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.709759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.824839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:11.939232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.052812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.165944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.277787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.384732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.492690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.604838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.722884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.834773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:12.943739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.046336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.143389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.250058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.352679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.445153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.547856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.670641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.792630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:13.908355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:14.019968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:14.130995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:14.245124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:14.367046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:14.507017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:14.644963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:14.809618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:15.136712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:15.397350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:15.610448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:15.756420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:15.904853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:16.093273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:16.252250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:16.401245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:16.682702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:16.867627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:17.026458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:17.183460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:17.315616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:17.794812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:17.974025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:18.124367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:18.254567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:18.400382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:18.519417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:18.643506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:18.766512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:18.897608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.016581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.120621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.233152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.398632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.514985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.638037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.744634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.855052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:19.962028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.072059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.181357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.301461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.416375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.528786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.642084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.755605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.867322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:20.979513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.093785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.198018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.306024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.426793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.549195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.650050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.757232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.862388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:21.967592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:22.077005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:22.195754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:22.317132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:22.433255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:22.546565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:22.686245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-19T11:55:45.156738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-19T11:55:45.250950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-19T11:55:52.958891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-19T11:55:53.269612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-19T11:55:53.578083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-19T11:55:53.890085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-19T11:55:54.134678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-19T11:55:45.537741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-19T11:55:46.156814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-19T11:55:46.829160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-19T11:55:46.945373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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